BiopharmaStudySampleChecker

Biopharma Study Sample

Liquid Biopsy жидкостная биопсия cfDNA ctDNA CTC exosomes NGS qPCR

Utility description: Biopharma Study Sample

Biopharma Study Sample Checker — Biopharma Study Sample Integrity Control

ℹ️ Utility performs comprehensive verification of preclinical and clinical study samples against protocol and GLP/GCP standards:
• Informed Consent: Mandatory verification of signed IC for each clinical sample.
• Temperature Integrity: Control of storage and transportation condition compliance.
• Chain of Custody: Verification of continuous documentation trail for sample accountability.
• Protocol Deviations: Recording and classification of any collection/storage procedure violations.

⚠️ IMPORTANT:
• Use of samples without confirmed consent is a gross violation of GCP and ethical standards.
• Temperature breach may render sample unsuitable for PK/PD or ADA analysis.

Usage:
BiopharmaStudySampleChecker.exe → demo mode (console output)
BiopharmaStudySampleChecker.exe input.csv output.json → evaluate your data

Input format:
StudyID,SubjectID,SampleID,MatrixType,Timepoint,CollectionDate,ReceiptDate,StorageTemp_C,MinStorageTemp_C,MaxStorageTemp_C,ConsentVerified,ChainOfCustodyComplete,ProtocolDeviation

Example:
STUDY-001,SUBJ-01,SMP-001,Serum,Pre-dose,2026-06-01 08:00,2026-06-01 14:00,-70,-80,-60,true,true,

📍 Scope of Application (Usage Where):
• Clinical Trials (Phase I-III): Sample receipt at central laboratory.
• Preclinical Studies (GLP): Toxicokinetics and TK/PD sample control.
• Biobanking: Inventory and quality check of stored samples.
• Audits and Inspections: Confirmation of sample collection practice compliance with protocol.

— WHY IS THIS NEEDED?
Study data quality directly depends on sample quality.
Pre-analytical errors (mislabeling, temperature breach, missing consent) cannot be corrected retrospectively.
Automated verification prevents use of compromised samples and protects overall study integrity.

⚠️ CRITICAL:
• Consent: Missing verification = automatic REJECT.
• Temperature: Even few degrees deviation may denature protein therapeutics.
• CoC: Broken chain of custody questions sample origin.
• Deviations: All deviations must be documented and assessed by PI/Sponsor.

Key features:
• Multi-level verification (Consent → Temp → CoC → Deviation)
• Automatic status classification (Accepted/Quarantine/Rejected)
• Support for various matrix types and timepoints
• Report generation for auditors and sponsors
• Compliance with ICH E6(R2) and GLP requirements

Critical parameters:
• Consent Verified: True
• Storage Temp: Within established range
• Chain of Custody: Complete
• Protocol Deviation: None or documented

💡 Usage tips:
1. EDC Integration: Synchronize IC data from electronic data capture system.
2. Temperature Loggers: Use digital loggers for objective condition confirmation.
3. Staff Training: Regularly train coordinators on collection and labeling rules.
4. Documentation: Save all reports as part of Trial Master File.
5. Escalation: Immediately report IC violations to ethics committee.

⚠️ Note: This utility is a pre-analytical quality control tool. It does not replace clinical trial monitoring but ensures reliability of input data for laboratory analysis.

input.csv

StudyID,SubjectID,SampleID,MatrixType,Timepoint,CollectionDate,ReceiptDate,StorageTemp_C,MinStorageTemp_C,MaxStorageTemp_C,ConsentVerified,ChainOfCustodyComplete,ProtocolDeviation
STUDY-PH1-2026-001,SUBJ-001,SMP-001-PRE,Serum,Pre-dose,2026-06-01 08:00:00,2026-06-01 14:00:00,-70,-80,-60,true,true,
STUDY-PH1-2026-001,SUBJ-002,SMP-002-1H,Plasma,1h Post-dose,2026-06-02 09:00:00,2026-06-03 10:00:00,-45,-80,-60,true,false,Temperature excursion during transport
STUDY-PH1-2026-001,SUBJ-003,SMP-003-24H,Serum,24h Post-dose,2026-06-03 08:00:00,2026-06-03 12:00:00,-75,-80,-60,false,true,Consent pending verification

URS & FS — User Requirements and Functional Specification

This document describes the controlled interface and behaviour of BiopharmaStudySampleChecker for Biopharma Study Sample Checker.

Domain limits URS input.csv FS IQ/OQ/PQ

Domain limits and critical parameters

Key fragments from the source description are shown below. Before production use, limits must be verified against the approved specification, registration dossier and local SOPs.

⚠️ CRITICAL:
• Deviations: All deviations must be documented and assessed by PI/Sponsor.
• Compliance with ICH E6(R2) and GLP requirements
Critical parameters:

URS — User Requirements Specification

ID	Requirement	Criticality	Acceptance criterion
URS-001	The utility shall accept an input.csv file for Biopharma Study Sample Checker with headers defined in the data contract.	High	The file is processed without manual header editing.
URS-002	The utility shall perform deterministic QC evaluation without machine learning and without probabilistic conformance decisions.	High	Identical input data, rule version and configuration produce reproducible results.
URS-003	The utility shall validate mandatory fields, data types, ranges, units and domain plausibility.	High	Schema, conversion and range errors are explicitly reported.
URS-004	The utility shall apply domain limits and rules from the description, approved specification, registration dossier and local SOPs.	High	Each check has PASS/WARNING/FAIL and a clear message.
URS-005	The utility shall generate output.json with machine-readable results, source values, warnings, failures and critical findings.	High	JSON is suitable for LIMS/ELN/MES integration and QA/QC review.
URS-006	The utility shall preserve traceability between batch/sample, input file, applied rules and final status.	High	Output contains identifiers, checked parameters and audit metadata.
URS-007	The documentation shall support IQ/OQ/PQ, CSV/CSA and review by internal QA or inspectors.	Medium	URS, FS, input/output contract and test scenarios are supplied with the utility.
URS-008	The utility shall be used as a QC decision-support tool and not as a substitute for approved specifications and QA/QP release decision.	Medium	Documentation states change control and limit-verification expectations.

input.csv contract

#	Field	Type	Sample	Purpose
1	`StudyID`	string / controlled vocabulary	`STUDY-PH1-2026-001`	Controlled input parameter for deterministic QC rules.
2	`SubjectID`	string / controlled vocabulary	`SUBJ-001`	Controlled input parameter for deterministic QC rules.
3	`SampleID`	string / controlled vocabulary	`SMP-001-PRE`	Sample or laboratory specimen identifier.
4	`MatrixType`	string / controlled vocabulary	`Serum`	Controlled input parameter for deterministic QC rules.
5	`Timepoint`	string / controlled vocabulary	`Pre-dose`	Time parameter for process, incubation, storage or analytical workflow.
6	`CollectionDate`	string / controlled vocabulary	`2026-06-01 08:00:00`	Controlled input parameter for deterministic QC rules.
7	`ReceiptDate`	string / controlled vocabulary	`2026-06-01 14:00:00`	Controlled input parameter for deterministic QC rules.
8	`StorageTemp_C`	decimal	`-70`	Temperature profile/MKT; storage, shipping and stability parameter.
9	`MinStorageTemp_C`	decimal	`-80`	Temperature profile/MKT; storage, shipping and stability parameter.
10	`MaxStorageTemp_C`	decimal	`-60`	Temperature profile/MKT; storage, shipping and stability parameter.
11	`ConsentVerified`	string / controlled vocabulary	`true`	Controlled input parameter for deterministic QC rules.
12	`ChainOfCustodyComplete`	string / controlled vocabulary	`true`	Controlled input parameter for deterministic QC rules.
13	`ProtocolDeviation`	string / controlled vocabulary		Controlled input parameter for deterministic QC rules.

StudyID,SubjectID,SampleID,MatrixType,Timepoint,CollectionDate,ReceiptDate,StorageTemp_C,MinStorageTemp_C,MaxStorageTemp_C,ConsentVerified,ChainOfCustodyComplete,ProtocolDeviation
STUDY-PH1-2026-001,SUBJ-001,SMP-001-PRE,Serum,Pre-dose,2026-06-01 08:00:00,2026-06-01 14:00:00,-70,-80,-60,true,true,
STUDY-PH1-2026-001,SUBJ-002,SMP-002-1H,Plasma,1h Post-dose,2026-06-02 09:00:00,2026-06-03 10:00:00,-45,-80,-60,true,false,Temperature excursion during transport
STUDY-PH1-2026-001,SUBJ-003,SMP-003-24H,Serum,24h Post-dose,2026-06-03 08:00:00,2026-06-03 12:00:00,-75,-80,-60,false,true,Consent pending verification

Input validation rules

ID	Field	Rule	Criticality
VR-001	`StudyID`	The field shall match an approved dictionary or accepted string representation.	High
VR-002	`SubjectID`	The field shall match an approved dictionary or accepted string representation.	High
VR-003	`SampleID`	The field shall match an approved dictionary or accepted string representation.	High
VR-004	`MatrixType`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-005	`Timepoint`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-006	`CollectionDate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-007	`ReceiptDate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-008	`StorageTemp_C`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-009	`MinStorageTemp_C`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-010	`MaxStorageTemp_C`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-011	`ConsentVerified`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-012	`ChainOfCustodyComplete`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-013	`ProtocolDeviation`	The field shall match an approved dictionary or accepted string representation.	Medium

FS — Functional Specification

ID	Function	Implementation
FS-001	CLI execution	Support execution modes: demo mode without arguments and production mode input.csv output.json.
FS-002	CSV import	Read input.csv in UTF-8/CSV-compatible format and validate header and expected columns.
FS-003	Schema validation	Check mandatory fields, column count, unknown key fields and empty mandatory values.
FS-004	Type conversion	Convert numeric, flag and text values; invalid format is recorded as a row-level error.
FS-005	Domain rule engine	Apply rules for Biopharma Study Sample Checker, including critical limits from the description and approved specification.
FS-006	Status aggregation	Produce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance.
FS-007	JSON export	Write output.json with detailed checks, source values, warnings, failures and critical findings.
FS-008	Audit support	Keep result structure suitable for review, deviation investigation and calculation reproduction.
FS-009	Integration contract	Support the scenario LIMS/ELN/MES → input.csv → utility → output.json → portal/admin review.
FS-010	Error handling	Return explicit messages for missing file, empty CSV, invalid schema, output write failure and invalid format.

Example output.json

{
  "utilityId": "biopharmastudysamplechecker",
  "utilityFolder": "BiopharmaStudySampleChecker",
  "package": "LiquidBiopsy",
  "overallStatus": "PASS|WARNING|FAIL",
  "sourceFile": "input.csv",
  "processedAtUtc": "2026-06-10T00:00:00Z",
  "checks": [
    {
      "parameter": "StudyID",
      "value": "STUDY-PH1-2026-001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-001"
    },
    {
      "parameter": "SubjectID",
      "value": "SUBJ-001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-002"
    },
    {
      "parameter": "SampleID",
      "value": "SMP-001-PRE",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-003"
    },
    {
      "parameter": "MatrixType",
      "value": "Serum",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-004"
    },
    {
      "parameter": "Timepoint",
      "value": "Pre-dose",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-005"
    },
    {
      "parameter": "CollectionDate",
      "value": "2026-06-01 08:00:00",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-006"
    },
    {
      "parameter": "ReceiptDate",
      "value": "2026-06-01 14:00:00",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-007"
    },
    {
      "parameter": "StorageTemp_C",
      "value": "-70",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-008"
    },
    {
      "parameter": "MinStorageTemp_C",
      "value": "-80",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-009"
    },
    {
      "parameter": "MaxStorageTemp_C",
      "value": "-60",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-010"
    },
    {
      "parameter": "ConsentVerified",
      "value": "true",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-011"
    },
    {
      "parameter": "ChainOfCustodyComplete",
      "value": "true",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-012"
    }
  ],
  "criticalFindings": [],
  "warnings": [],
  "audit": {
    "inputHash": "sha256:<calculated at runtime>",
    "rulesVersion": "<utility executable version>",
    "documentation": "BiopharmaStudySampleChecker.documentation.html"
  }
}

Traceability matrix

URS	FS	Test	Evidence
URS-001	FS-001, FS-002	OQ-001	Verify execution and import of valid input.csv.
URS-002	FS-005, FS-006	OQ-004	Repeat the same dataset and compare output.json.
URS-003	FS-003, FS-004, FS-010	OQ-002, OQ-003	Verify missing columns and invalid types.
URS-004	FS-005, FS-006	OQ-004, PQ-001	Verify critical deviations on real/boundary data.
URS-005	FS-007, FS-009	OQ-005	Verify JSON schema and downstream-system suitability.
URS-006	FS-008	OQ-006	Verify identifiers and audit metadata.
URS-007	FS-008, FS-010	IQ-001, OQ-007	Verify documentation completeness and control evidence.
URS-008	FS-005, FS-008	PQ-002	Verify review workflow and no replacement of QA decision.

IQ/OQ/PQ test scenarios

ID	Scenario	Expected result
IQ-001	Verify executable, input.csv, documentation and checksum availability.	Delivery set is complete; version is recorded.
OQ-001	Valid sample row from input.csv.	PASS or acceptable WARNING according to rules.
OQ-002	Remove a mandatory CSV column.	Schema error or FAIL with missing-column reference.
OQ-003	Place a non-numeric value into a numeric field.	Type-conversion error with row/field reference.
OQ-004	Set a critical parameter outside the limit.	FAIL and critical finding.
OQ-005	Verify output.json structure.	All mandatory sections are present and JSON is valid.
OQ-006	Verify batch/sample traceability.	Input and result identifiers match.
PQ-001	Verify 3–5 real user batches/samples.	Result is confirmed by QC/QA review.
PQ-002	Verify deviation workflow and manual QA decision.	Utility supports review but does not replace approved decision.

QA/QC and change control

Do not rename columns without updating validator, documentation and test set.
Retain input.csv, output.json, executable version and checksum.
Before production use, perform IQ/OQ/PQ or equivalent CSV/CSA verification.
Critical limits shall be verified against the approved specification, registration dossier and local SOPs.
The utility provides structured QC decision support; final release decision remains with QA/QP and approved procedures.

Included in packages

Biopharmaceuticals Extended QC Suite

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Liquid Biopsy QC Suite

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